Personalizing debt collections : Combining reinforcement learning and field experiment

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationICIS 2020 Proceedings
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632553
Publication statusPublished - Dec 2021
Externally publishedYes

Publication series

NameInternational Conference on Information Systems, ICIS

Conference

Title41st International Conference on Information Systems (ICIS 2020)
LocationVirtual
PlaceIndia
Period13 - 16 December 2020

Abstract

Artificial intelligence (AI) brings about opportunities to revolutionize financial services. We focus on the loan debt collection context wherein collectors usually leverage private-information-based actions and follow a strict sequential collection strategy. We apply reinforcement learning to optimize the collection strategy with fine-grained data. The optimized results suggest loan platforms generally use collection actions less (by 63.39%) and more cautiously. Further borrower profiling analyses underscore the importance of personalization in debt collections. Moreover, we demonstrate the vast economic value of personalization in debt collections as it not only improves loan recovery rate (by 8.11%), but also enables platforms to allocate limited resources to cover more delinquent loans. A field experiment helps validate and quantify the economic value of the optimization algorithm in a real-world context. This study contributes to the literature of AI in FinTech, debt collections, and privacy. The findings also offer concrete, actionable, and cost-effective practical and policy implications.

Research Area(s)

  • Debt collection, Field experiment, Optimization, Personalization, Privacy, Reinforcement learning

Citation Format(s)

Personalizing debt collections : Combining reinforcement learning and field experiment. / Yang, Cenying; Lu, Tian; Li, Beibei; Lu, Xianghua.

ICIS 2020 Proceedings. Association for Information Systems, 2021. 2350 (International Conference on Information Systems, ICIS).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review